Modeling pandemic surges using an electric circuit analogue

The current pandemic has spread alarmingly fast across the planet, and many experts have been tasked with accurately tracking and predicting the spread of the initial virus and the subsequent variants. This can be challenging due to the number of factors affecting infection rates.

Nolasco et al. developed a model for virus spread derived from the same calculations used to model electrical circuits. They created a model circuit featuring a diode, a resistor, and a space-charge-limited current (SCLC) mechanism element and mapped the interactions between them to the trends observed during the pandemic.

The model fits well with infection rates observed in multiple countries and provides insights into how pandemics progress.

“We were able to analyze the evolution of the COVID-19 pandemic using mathematical models based on electronics engineering and applied physics,” said author Jairo Cesar Nolasco. “The trends defined by the number of cumulative daily COVID-19 cases reported for different countries behave similarly to the electrical characteristics that we usually measure in electronic devices.”

The analysis identified that the number of new COVID-19 cases at a certain point in the first pandemic wave fell into one of three phases. In the initial phase, the number of cases grew linearly, corresponding with the resistor element in the model circuit. At around 100 cases, the virus spread was dominated by exponential growth, represented by the diode element. In the third phase, as social distancing efforts and vaccines became more widespread, the growth curve was dominated by a power law term, coinciding with the SCLC element.

The team’s next step is to collaborate with epidemiologists and other experts to create an even more detailed model.

Source: “A simple electrical-circuit analogous phenomenological COVID-19 model valid for all observed pandemic phases,” by Jairo Cesar Nolasco, Jetro Tejeda Garcia, Andres Castro-Chacón, Alejandra Castro-Carranza, and Jürgen Gutowski, AIP Advances (2022). The article can be accessed at https://doi.org/10.1063/5.0078187 .